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REVIEW 3 major objections 7 minor 39 references

Unified dimensionality reduction techniques in chronic liver disease detection

T0 review · 3 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The authors report that a unified stack of LDA, FA, t-SNE and UMAP followed by Random Forest achieves 98.31% accuracy on the ILPD dataset, outperforming earlier published results.

desk verdict The reported accuracies are artifacts of synthetic-data contamination and pre-split oversampling, not measures of real ILPD performance. read the letter →

arxiv 2412.21156 v1 pith:JR5ORUMM submitted 2024-12-30 cs.LG

Anand Karna , Naina Khan , Rahul Rauniyar , Prashant Giridhar Shambharkar This is my paper · ORCID
classification cs.LG MSC 68T0562H30
keywords chronicliverdiseaseILPDdimensionalityreductionLDAfactoranalysist-SNEUMAPRandomForest
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a single integrated pipeline for predicting chronic liver disease from the Indian Liver Patient Dataset (ILPD): replace outliers by quartile values, balance classes by random oversampling, apply four dimensionality-reduction methods in sequence (LDA, then factor analysis, then t-SNE, then UMAP), scale the result, and feed it to a classifier. The authors' central claim is that this unified reduction stack lets a plain Random Forest reach 98.31% accuracy in 10-fold cross-validation and 95.79% accuracy in a train-test split, both better than previously published results on the same dataset. The authors also report that Random Forest beats logistic regression, multi-layer perceptron, and k-nearest neighbours on all metrics in both protocols. The contribution is presented as the ordering and integration of the four reduction methods rather than a new classifier.

What carries the argument

The load-bearing object is the sequential feature-integration pipeline: LDA ($S_w^{-1}S_b w = \lambda w$) and factor analysis ($S = LL^T + \Psi$) are applied first to condense the 10 raw features; their outputs are concatenated, then t-SNE (minimizing the KL divergence between high- and low-dimensional neighbor distributions) and a three-component UMAP embedding are applied, and finally StandardScaler normalizes the result. The authors' stated rationale is that removing outliers before, rather than after, dimensionality reduction protects t-SNE's local-structure preservation from extreme values, and that the linear-then-nonlinear order captures both global class structure and local neighborhood structure.

What would settle it

Re-run the pipeline on the original 583-patient ILPD without the 1,000 synthetic records; if accuracy drops from 98.31% toward the 88–95% range of prior published ILPD results, then the synthetic samples, not the reduction stack, are responsible for the claimed performance.

Watch

Extended reading notes

Core claim

The central claim is that a carefully ordered combination of linear and nonlinear dimensionality reduction — LDA and FA first, then t-SNE and UMAP, followed by standard scaling — transforms the ILPD feature space into a representation on which a plain Random Forest achieves near-perfect separation of liver-disease from non-liver-disease patients. In the authors' framing, LDA maximizes class separability, FA recovers latent factors, t-SNE preserves local neighborhoods, and UMAP preserves local and global manifold structure; together they are said to yield a unified low-dimensional view in which the classifier generalizes well. The paper reports 98.31% mean accuracy, 98.20% precision, 98.42% recall, and 98.315% F1 in 10-fold cross-validation, and 95.79% accuracy, 97.79% precision, 93.67% recall, and 99.53% AUC in the train-test split, with Random Forest the best of four classifiers in both protocols.

Load-bearing premise

The reported accuracies rest on the assumption that training and evaluating on a dataset that includes 1,000 synthetic patient records, generated with undisclosed distributions and labels alongside the real 583-record ILPD, measures how well the models would perform on real liver-disease patients.

Editorial extensions

If this is right

  • Random Forest on the unified reduction representation is the best of the four classifiers tested in both evaluation protocols.
  • The ordering of preprocessing matters: outlier replacement before dimensionality reduction is claimed to improve t-SNE stability and is a deliberate point of divergence from prior work.
  • The combined linear-then-nonlinear reduction is claimed to capture both global and local structure, which the authors expect to transfer to other high-dimensional clinical tabular datasets.
  • The high cross-validation and train-test accuracies are offered as evidence that the model resists overfitting and is suitable for real-world screening applications.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the 1,000 synthetic rows are concatenated before the train-test split, their structure is likely embedded in both training and test folds; a reader should treat the 98.31% figure as a property of the combined synthetic-plus-real population, not of the ILPD population alone.
  • The paper does not report the distribution, separability, or label balance of the synthetic samples; if those samples are trivially separable, they would inflate accuracy regardless of the dimensionality-reduction stack.
  • A direct ablation — running Random Forest on the raw scaled features without LDA/FA/t-SNE/UMAP, and on each reduction method alone — would test whether the four-stage stack is necessary or whether one nonlinear method such as UMAP suffices; the paper does not include such an ablation.
  • The confusion matrix reported for the train-test split contains 666 test instances, which matches a 75/25 split of the oversampled real data (2,664 rows) rather than of the combined set with synthetic rows; the paper does not clarify which rows were in the test set.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The paper proposes a pipeline for chronic liver disease detection on the Indian Liver Patient Dataset (ILPD) that combines linear and nonlinear dimensionality reduction techniques (LDA, FA, t-SNE, UMAP) with standard classifiers. The authors report that Random Forest achieves 98.31% accuracy in 10-fold cross-validation and 95.79% accuracy in a train-test split, which they claim outperforms prior state-of-the-art results on ILPD. The methodology includes imputation, random oversampling, generation and concatenation of 1000 synthetic samples, IQR-based outlier replacement, feature integration, and scaling.

Significance. If the reported results were valid, the paper would offer a useful empirical comparison of combined linear/nonlinear dimensionality reduction techniques on a widely used clinical dataset, and the proposed pipeline would merit attention. The manuscript provides mathematical formulations for the dimensionality reduction methods and describes the preprocessing steps in detail. However, the central evaluation is compromised: the test data are not drawn from the real ILPD patient distribution because 1000 synthetic samples are concatenated with the real records and random oversampling is applied before the data splits. Consequently, the reported accuracies do not measure generalization to real chronic liver disease patients. The paper would need a complete re-analysis on the original ILPD, with oversampling confined to the training folds and without synthetic concatenation, to support its claims.

major comments (3)
  1. [§3.2.2–3.2.3, Eqs. (1)–(2)] The evaluation dataset is contaminated by synthetic data. Section 3.2.2 describes generating 1000 samples with 10 features (5 informative, 3 redundant, 2 repeated) and Section 3.2.3 concatenates them with the 583 real ILPD records. No generative distribution, class-balance rule, or parameters are given for this synthetic dataset, and no argument is provided that these points come from the same feature-label joint distribution as ILPD. The reported 98.31% and 95.79% accuracies in Tables 2 and 3 are therefore computed on a dataset in which roughly 63% of rows are fabricated, so they cannot be interpreted as measurements of performance on real chronic liver disease patients. This invalidates the paper's central claim.
  2. [§3.2.1, §3.5.1–3.5.2, Fig. 12] Oversampling is applied before the train-test split and cross-validation, which creates leakage through duplicated rows that can appear in both training and test sets. The confusion matrix in Fig. 12 sums to 666 instances (313+329+5+19), which is exactly 25% of 2664 rows, matching a 25% stratified split on the combined, oversampled dataset; a 25% split of the original 583-record ILPD would contain about 146 instances. Thus the test set contains synthetic and duplicated examples, and the reported generalization measures do not reflect the original ILPD distribution. The evaluation protocol must be redone without pre-split augmentation.
  3. [§4.3, Table 4] The comparative analysis is not valid evidence of state-of-the-art performance. Prior results listed in Table 4 were obtained on the original ILPD or its standard preprocessed versions, whereas the proposed method is evaluated on a dataset augmented with 1000 synthetic rows and balanced via oversampling. Because the evaluation set differs, the accuracy advantage over the cited baselines is not attributable to the proposed dimensionality-reduction pipeline. A fair comparison would require the same protocol on the same data.
minor comments (7)
  1. [Abstract, §1] The abstract and introduction claim performance on the Indian Liver Patient Dataset but do not disclose that the evaluation included 1000 synthetic samples; this should be prominently stated.
  2. [§3.2.4] The text says k (1.5 or 3) determines sensitivity for the IQR bounds but never states which value was used in the experiments, and the phrase 'second quartile values' is an unclear way to refer to the median.
  3. [§3.1] The mapping of the target variable is ambiguous: the text says 'the values 0 and 1 were mapped to represent the presence and absence of liver conditions, respectively,' but ILPD's original labels are 1 and 2; the exact mapping should be stated explicitly.
  4. [§4.1.5, Fig. 9] Figure 9 is described in the text as a calibration curve, but the caption reads 'Learning curve for different algorithms in train-test split'; the caption and the referenced section should be consistent.
  5. [§4.1.7] The confusion matrix discussion says the model identified '313 cases of liver disease (LD) and 329 cases of non-liver disease (NLD),' which is inconsistent with the 0/1 mapping defined in Section 3.1; clarify which class label corresponds to disease.
  6. [§3.3.3–3.3.4] Hyperparameters for t-SNE (perplexity, number of iterations) and UMAP (n_neighbors, min_dist) are not reported, even though fixed random states are mentioned; this hampers reproducibility.
  7. [§3.5.2] There is a grammatical error: '10-fold cross-validation was also used to assess the of the model' should read 'to assess the model.'

Circularity Check

2 steps flagged · score 6.0 of 10

Synthetic-data concatenation and pre-split oversampling make the headline accuracies self-referential rather than ILPD measurements.

  1. other [Section 3.2.2 'Data Simulation' and Section 3.2.3 'Concatenation' (Eqs. 1-2); evaluation in Section 3.5.1 and Figure 12]
    "We generated a synthetic dataset with 1000 samples and 10 features, where 5 features provided relevant information, 3 were redundant, and 2 were repeated. This dataset was produced specifically for a binary classification assignment. The synthetic dataset was then concatenated with the original dataset."

    The test set for the headline accuracy is drawn from the combined dataset, which includes 1000 rows the authors generated. The confusion matrix in Figure 12 sums to 666 (313+329+5+19), matching a 25% split of the 2664-row oversampled dataset, not a 25% split of the original 583-record ILPD. Hence the claimed '98.31% on ILPD' is actually a score on a self-constructed dataset whose synthetic rows have undisclosed distributions and label rules. The benchmark is defined by the authors' own data generation, so the validation is self-referential rather than an independent measurement on real patients.

  2. fitted input called prediction [Section 3.2.1 'Imputation and Oversampling' vs. Section 3.5.1 'Train-Test Split' and Section 3.5.2 'Cross Validation']
    "With RandomOverSampler, we were able to effectively balance the distribution of classes. After oversampling, the class distribution was 1334 (0) and 1330 (1). ... We used stratified splitting to maintain class proportions when separating the dataset into training (75%) and testing (25%) subsets."

    RandomOverSampler is applied before the 75/25 split and before 10-fold cross-validation, so duplicated minority-class rows can appear in both training and test partitions. Test accuracy then partly measures recognition of exact training copies rather than generalization to new patients. The reported 95.79% train-test and 98.31% cross-validation figures are inflated by construction: the 'test' predictions are not statistically independent of the training data.

full rationale

The reported state-of-the-art accuracies are not independent measurements of ILPD performance. Section 3.2.2 generates 1000 synthetic samples with undisclosed distributions and label rules, and Section 3.2.3 concatenates them with the patient data (Eqs. 1-2). Section 3.2.1 applies RandomOverSampler before the Section 3.5 splits, so minority-class duplicates can appear in both training and test partitions. The confusion matrix in Figure 12 sums to 666, which matches a 25% split of the 2664-row oversampled dataset and not a 25% split of the original 583-record ILPD. Thus the 98.31% CV and 95.79% train-test results are computed on a self-constructed dataset rather than on held-out real patients. No load-bearing self-citation or imported uniqueness theorem was found; the circularity is in the evaluation protocol, not in the references. The dimensionality-reduction chain itself is standard and non-circular.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The central claim rests on a fabricated data artifact (1000 synthetic samples of undisclosed distribution) plus several hand-set dimensionality reduction hyperparameters. No new physical or conceptual entities are introduced; the synthetic dataset is a data-construction choice rather than an invented theoretical entity.

free parameters (6)
  • Synthetic dataset size = 1000 samples x 10 features
    Chosen by hand with no justification; concatenating this fabricated data is the main driver of the reported high accuracy. Section 3.2.2.
  • FA factor count = 3
    Set in Section 3.3.2 without a stated criterion; affects the reduced feature set fed to t-SNE.
  • t-SNE output dimension = 3
    Instantiated with three output dimensions in Section 3.3.3; not justified.
  • UMAP component count = 3
    Configured as a three-component UMAP in Section 3.3.4; no justification.
  • IQR outlier multiplier k = undisclosed (1.5 or 3)
    Section 3.2.4 says k is 1.5 or 3, but the actual value used in the experiments is not reported.
  • Oversampling target class sizes = 1334 (0) and 1330 (1)
    RandomOverSampler is used to balance classes; the duplication rule and its interaction with the train-test split are not specified. Section 3.2.1.
assumptions (3)
  • ad hoc to paper The 1000 synthetic samples, generated by an undisclosed procedure, are a valid substitute for real patient data when concatenated with ILPD.
    This is the load-bearing premise for all reported accuracies; the paper provides no evidence that the synthetic distribution resembles ILPD. Sections 3.2.2 and 3.2.3.
  • ad hoc to paper Evaluating models on the combined synthetic-plus-real dataset measures generalization to real chronic liver disease patients.
    The test folds contain synthetic and oversampled duplicated rows, so the reported metrics do not estimate real-world performance. Sections 3.5.1 and 3.5.2.
  • domain assumption t-SNE and UMAP embeddings preserve the class-discriminative structure needed for classification.
    The reduced features are fed to classifiers without validating that the nonlinear embeddings retain relevant signal. Sections 3.3.3 and 3.3.4.

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Cite this review

Pith. "Pith review of Unified dimensionality reduction techniques in chronic liver disease detection." pith.science (2026). https://pith.science/paper/JR5ORUMM

@misc{pith2026241221156,
  author       = {Pith},
  title        = {Pith review of: Unified dimensionality reduction techniques in chronic liver disease detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JR5ORUMM}},
  note         = {Machine review of arXiv:2412.21156}
}
read the original abstract

Globally, chronic liver disease continues to be a major health concern that requires precise predictive models for prompt detection and treatment. Using the Indian Liver Patient Dataset (ILPD) from the University of California at Irvine's UCI Machine Learning Repository, a number of machine learning algorithms are investigated in this study. The main focus of our research is this dataset, which includes the medical records of 583 patients, 416 of whom have been diagnosed with liver disease and 167 of whom have not. There are several aspects to this work, including feature extraction and dimensionality reduction methods like Linear Discriminant Analysis (LDA), Factor Analysis (FA), t-distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). The purpose of the study is to investigate how well these approaches work for converting high-dimensional datasets and improving prediction accuracy. To assess the prediction ability of the improved models, a number of classification methods were used, such as Multi-layer Perceptron, Random Forest, K-nearest neighbours, and Logistic Regression. Remarkably, the improved models performed admirably, with Random Forest having the highest accuracy of 98.31\% in 10-fold cross-validation and 95.79\% in train-test split evaluation. Findings offer important new perspectives on the choice and use of customized feature extraction and dimensionality reduction methods, which improve predictive models for patients with chronic liver disease.

Figures

Figures reproduced from arXiv: 2412.21156 by the authors.

Figure 1
Figure 1. A flow diagram depicting the methodology used in the study standard for machine learning research, facilitating the creation and assessment of models intended to predict the presence or absence of liver disease. We converted the ’Target’ variable into a binary format, which typically indicates whether or not liver disease has been diagnosed.With this modification, the val￾ues 0 and 1 were mapped to represent the pre… view at source ↗
Figure 4
Figure 4. Precision-Recall curves in 10-fold cross validation [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Precision-Recall curves in Train-Test Split Random Forest demonstrated its superior ability to retrieve relevant instances without sacrificing pre￾cision in the train-test split analysis, as evidenced by its ability to maintain higher precision levels across a range of recall thresholds. While KNN demonstrated remarkable precision but marginally worse recall per￾formance, MLP demonstrated balanced but moderate perfo… view at source ↗
Figures from the paper (7 more)
Figure 6
Figure 6. Figure 6: Bar chart representing evaluation metrics for each algorithm in 10-fold cross validation [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Bar chart representing evaluation metrics for each algorithm in Train-Test Split Forest) and 92.30% (KNN). In cross-validation, Ran￾dom Forest peaked at 98.315% F1-Score, matching its AUC and accuracy. In the train-test split analysis, Random Forest excelled across all…
Figure 8
Figure 8. Figure 8: Runtime of different algorithms 10 [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 10
Figure 10. Figure 10: Learning curve for random forest in train-test split [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 9
Figure 9. Figure 9: Learning curve for different algorithms in train-test split The use of a calibration curve is justified by its capacity to provide information about model relia￾bility by illuminating the relationship between ex￾pected probabilities and actual results. Isotonic Re￾gres…
Figure 11
Figure 11. Figure 11: Learning curve for random forest in 10-fold cross validation The CV score increased dramatically to 98.31% during the learning curve plotting with a 10-fold cross-validation, starting at about 77%. This addi￾tional knowledge strengthens the model’s robustness by highl…
Figure 12
Figure 12. Figure 12: Confusion matrix for random forest in train-test split These results not only showed that the model could produce accurate predictions, but they also high￾lighted some areas that still required work. The confusion matrix was a useful tool for a more ac￾curate and nuan…

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Works this paper leans on

39 extracted references · 33 canonical work pages

  1. [1]

    Cheemerla, S., & Balakrishnan, M. (2021b). Global Epidemiology of Chronic Liver Disease. Clinical Liver Disease, 17(5) https://doi.org/10. 1002/cld.1061

    work page 2021

  2. [2]

    K., Devarbhavi, H., Eaton, J

    Asrani, S. K., Devarbhavi, H., Eaton, J. E., & Kamath, P . S. (2019). Burden of liver diseases in the world. Journal of Hepatology, 70(1), 151–171. https://doi.org/10.1016/j.jhep.2018.09.014

    work page doi:10.1016/j.jhep.2018.09.014 2019

  3. [3]

    Umbare, R., Ashtekar, O., Nikhal, A., Pagar, B., & Zare, O. (2023). Prediction & Detection of Liver Diseases using Machine Learning. IEEE 3rd International Conference on TEMSMET. https://doi.org/10.1109/temsmet56707.2023. 10150135

    work page arXiv 2023

  4. [4]

    P ., Suman, R., & Rab, S

    Javaid, M., Haleem, A., Singh, R. P ., Suman, R., & Rab, S. (2022). Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks, 3, 58–73. https://doi.org/10.1016/j.ijin.2022.05.002

    work page doi:10.1016/j.ijin.2022.05.002 2022

  5. [5]

    Liu, Y., & Chen, M. (2022). Epidemiology of liver cirrhosis and associated complications: Current knowledge and future directions. World Journal of Gastroenterology, 28(41), 5910–5930. https: //doi.org/10.3748/wjg.v28.i41.5910

    work page doi:10.3748/wjg.v28.i41.5910 2022

  6. [6]

    M., Blissett, D

    Younossi, Z. M., Blissett, D. B., Blissett, R., Henry, L., Stepanova, M., Younossi, Y., Racila, A., Hunt, S., & Beckerman, R. (2016). The economic and clinical burden of nonalcoholic fatty liver disease in the United States and Europe. Hepatology, 64(5), 1577–1586. https://doi.org/10.1002/hep. 28785 14

    work page doi:10.1002/hep 2016

  7. [7]

    M., Dita, M

    Popa, S., Ismaiel, A., Abenavoli, L., Padureanu, A. M., Dita, M. O., Bolchis, R., Munteanu, M., Brata, V . D., Pop, C., Bosneag, A., Dumitra¸ scu, D. I., Bârsan, M., & David, L. (2023). Diagnosis of liver fibrosis using Artificial Intelligence: A Sys- tematic review. Medicina-lithuania, 59(5), 992. https://doi.org/10.3390/medicina59050992

    work page doi:10.3390/medicina59050992 2023

  8. [8]

    A., Hughes, C., & Shah, V

    Ahn, J., Connell, A., Simonetto, D. A., Hughes, C., & Shah, V . H. (2021). Application of artifi- cial intelligence for the diagnosis and treatment of liver diseases. Hepatology, 73(6), 2546–2563. https://doi.org/10.1002/hep.31603

    work page doi:10.1002/hep.31603 2021

Show all 39 references
  1. [9]

    (2022) Liver Disease Detection using Machine Learning Techniques

    Bhupathi D, Tan C N-L, Tirumula S.S, Ray S.K. (2022) Liver Disease Detection using Machine Learning Techniques. The Computing and In- formation Technology Research and Education New Zealand (CITRENZ) Springer

    2022

  2. [10]

    Mohan, V ., Dhayanand, S. (2015). Liver Disease Prediction using SVM and Naïve Bayes Algo- rithms

    2015

  3. [11]

    Wu, J., Lin, S., Wan, B., Velani, B., & Zhu, Y. (2019). Pyroptosis in Liver Disease: New Insights into Disease Mechanisms. Aging and Disease, 10(5), 1094. https://doi.org/10.14336/ad.2019. 0116

    2019 doi

  4. [12]

    H., & Reza, M

    Amin, R., Yasmin, R., Ruhi, S., Rahman, M. H., & Reza, M. S. (2023). Prediction of chronic liver dis- ease patients using integrated projection based statistical feature extraction with machine learn- ing algorithms. Informatics in Medicine Un- locked, 36, 101155. https://doi....

    2023

  5. [13]

    A., & Freeman, L

    Batarseh, F. A., & Freeman, L. (2022). AI Assur- ance: Towards Trustworthy, Explainable, Safe, and Ethical AI. Elsevier. ISBN 978-0-323-91919-7

    2022

  6. [14]

    Tavakol, M., & Wetzel, A. P . (2020). Factor Anal- ysis: a means for theory and instrument devel- opment in support of construct validity. Interna- tional Journal of Medical Education, 11, 245–247. https://doi.org/10.5116/ijme.5f96.0f4a

    2020 doi

  7. [15]

    Cai, T., & Ma, R. (2021). Theoretical Foundations of T-SNE for Visualizing High-Dimensional Clus- tered Data. arXiv (Cornell University). https: //doi.org/10.48550/arxiv.2105.07536

    2021 doi arXiv

  8. [16]

    Singh, J., Bagga, S., & Kaur, R. (2020). Software- based Prediction of Liver Disease with Feature Selection and Classification Techniques. Proce- dia Computer Science, 167, 1970–1980. https: //doi.org/10.1016/j.procs.2020.03.226

    2020 doi

  9. [17]

    Wang, N., Yu, Y., Huang, D., Xu, B., Liu, J., Li, T., Xue, L., Zengyu, S., Chen, Y., & Wang, J. (2015). Pulse Diagnosis signals analysis of fatty liver disease and cirrhosis patients by using machine learning. The Scientific World Journal, 2015, 1–9. https://doi.org/10.1155/20...

    2015 doi

  10. [18]

    Kumar, Y., & Sahoo, G. (2013). Prediction of different types of liver diseases using rule based classification model. Technology and Health Care, 21(5), 417–432. https://doi.org/10.3233/ thc-130742

    2013

  11. [19]

    Dritsas, I., & Trigka, M. (2023). Supervised Ma- chine learning models for liver disease risk pre- diction. Computers, 12(1), 19. https://doi.org/ 10.3390/computers12010019

    2023 doi

  12. [20]

    Muthuselvan, S., Rajapraksh, S., Somasundaram, K., & Karthik, K. (2018). Classification of liver pa- tient dataset using machine learning algorithms. International Journal of Engineering & Technol- ogy, 7(3.34), 323. https://doi.org/10.14419/ijet. v7i3.34.19217

    2018 doi

  13. [21]

    Babu, M. S. P ., Ramjee, M., Katta, S., & Swapna, K. (2016). Implementation of partitional clus- tering on ILPD dataset to predict liver disor- ders. ICSESS. https://doi.org/10.1109/icsess. 2016.7883256

    2016

  14. [22]

    G., Kumar, P ., Reddy, S

    Sujith, J. G., Kumar, P ., Reddy, S. J. M., & Kanhe, A. (2023). Computative analysis of vari- ous techniques for classification of liver disease. Journal of Physics: Conference Series, 2466(1), 012035. https://doi.org/10.1088/1742-6596/ 2466/1/012035

    2023 doi

  15. [23]

    Sivasangari, A., Reddy, B. J. K., Kiran, A., & Ajitha, P . (2020). Diagnosis of Liver Disease us- ing Machine Learning Models. 2020 Fourth In- ternational Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). https: //doi.org/10.1109/i-smac49090.2020.9243375

    2020

  16. [24]

    E., McKnight, K

    McKnight, P . E., McKnight, K. M., Sidani, S., & Figueredo, A. J. (2007). Missing data: A Gentle Introduction. Guilford Press

    2007

  17. [25]

    Alpaydin, E. (2014). Introduction to machine learning. MIT Press

    2014

  18. [26]

    Muixí, A., Garcia-Gonzalez, A., Zlotnik, S., & DiEz, P . (2023). Linear and nonlinear dimen- sionality reduction of biomechanical models. In Elsevier eBooks (pp. 23–44). https://doi.org/10. 1016/b978-0-32-389967-3.00004-4

    2023

  19. [27]

    Tharwat, A., Gaber, T., Ibrahim, A., & Hassanien, A. E. (2017). Linear discriminant analysis: A detailed tutorial. Ai Communications, 30(2), 169–190. https://doi.org/10.3233/aic-170729

    2017 doi

  20. [28]

    J., & Baltes, P

    Smelser, N. J., & Baltes, P . B. (2001). International Encyclopedia of the Social & Behavioral Sciences

    2001

  21. [29]

    A., & Wichern, D

    Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical analysis. Prentice Hall

    2007

  22. [30]

    Zhou, H., Wang, F., & Tao, P . (2018). t- Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations. Journal of Chemi- cal Theory and Computation, 14(11), 5499–5510. 15 https://doi.org/10.1021/acs.jctc.8b00652

    2018 doi

  23. [31]

    van der Maaten, L. J. P ., & Hinton, G. E. (2008). Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9(nov), 2579-2605

    2008

  24. [32]

    McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for dimensionality reduction. arXiv preprint arXiv:1802.03426

    2018 arXiv

  25. [33]

    Vaidya, V ., & Vaidya, J. (2022). Impact of di- mensionality reduction on Outlier Detection: an Empirical Study. IEEE 4th Int Conf Trust Priv Secur Intell Syst Appl (2022). https://doi.org/ 10.1109/tps-isa56441.2022.00028

    2022

  26. [34]

    Halladin-Dabrowska, A., Kania, A., & Kope´ c, D. (2019). The T-SNE algorithm as a tool to improve the quality of reference data used in accurate mapping of heterogeneous Non-Forest vegetation. Remote Sensing, 12(1), 39. https: //doi.org/10.3390/rs12010039

    2019 doi

  27. [35]

    A., Lakshmi, K

    Niha, S. A., Lakshmi, K. J., Blessi, P . G., Lakshmi, T. S., Chowdary, Y. M., & Rao, M. P . (2023). A comparison of machine learning algorithms for predicting liver disease. International Journal for Research in Applied Science and Engineering Technology, 11(3), 2221–2230. htt...

    2023

  28. [36]

    Anthonysamy, V ., & Babu, S. K. K. (2023). Multi perceptron neural network and voting classi- fier for liver disease dataset. IEEE Access, 11, 102149–102156. https://doi.org/10.1109/access. 2023.3316515

    2023

  29. [37]

    Ghosh, M., Raihan, M. M. S., Raihan, M., Ak- ter, L., Bairagi, A. K., Alshamrani, S. S., & Ma- sud, M. (2021). A comparative analysis of ma- chine learning algorithms to predict liver dis- ease. Intelligent Automation and Soft Comput- ing, 30(3), 917–928. https://doi.org/10.32...

    2021

  30. [38]

    Panwar, V ., Choudhary, N., Mittal, S., & Sahu, G. (2021). Review of liver disease prediction using machine learning algorithm. Journal of Emerging Technology and Innovative Research (JETIR), 8(2), 2349–5162

    2021

  31. [39]

    A., Joseph, C., & Kannan, A

    Kuzhippallil, M. A., Joseph, C., & Kannan, A. (2020). Comparative Analysis of Machine Learn- ing Techniques for Indian Liver Disease Patients. ICACCS. https://doi.org/10.1109/icaccs48705. 2020.9074368 16

    2020

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.